A newly expanded and updated edition of the trading classic, Design, Testing, and Optimization of Trading Systems Trading systems expert Robert Pardo is back, and in The Evaluation and Optimization of Trading Strategies, a thoroughly revised and updated edition of his classic text Design, Testing, and Optimization of Trading Systems, he reveals how he has perfected the programming and testing of trading systems using a successful battery of his own time-proven techniques. With this book, Pardo delivers important information to readers, from the design of workable trading strategies to measuring issues like profit and risk. Written in a straightforward and accessible style, this detailed guide presents traders with a way to develop and verify their trading strategy no matter what form they are currently using–stochastics, moving averages, chart patterns, RSI, or breakout methods. Whether a trader is seeking to enhance their profit or just getting started in testing, The Evaluation and Optimization of Trading Strategies offers practical instruction and expert advice on the development, evaluation, and application of winning mechanical trading systems.

The title says it all. Concise, straight to the point guidance on developing a winning computer trading system. Copyright © Libri GmbH. All rights reserved.

The Encyclopedia of Trading Strategies is for traders who want to take the next step to consistently profitable trading. The authors--themselves seasoned veterans of the futures trading arena--pinpoint the trading methods and strategies that have been shown to produce market-beating returns. Their rigorous and systematic backtesting of each method, using the same sets of markets and analytic techniques, provides a scientific, system-based approach to system development...to help you assemble the trading system that will put you on the road to becoming a more consistently profitable trader.

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

"Trading Systems" offers an insight into what a trader should know and do in order to achieve success on the markets.

The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

The first and only book of its kind, Automated Options Trading describes a comprehensive, step-by-step process for creating automated options trading systems. Using the authors’ techniques, sophisticated traders can create powerful frameworks for the consistent, disciplined realization of well-defined, formalized, and carefully-tested trading strategies based on their specific requirements. Unlike other books on automated trading, this book focuses specifically on the unique requirements of options, reflecting philosophy, logic, quantitative tools, and valuation procedures that are completely different from those used in conventional automated trading algorithms. Every facet of the authors’ approach is optimized for options, including strategy development and optimization; capital allocation; risk management; performance measurement; back-testing and walk-forward analysis; and trade execution. The authors’ system reflects a continuous process of valuation, structuring and long-term management of investment portfolios (not just individual instruments), introducing systematic approaches for handling portfolios containing option combinations related to different underlying assets. With these techniques, it is finally possible to effectively automate options trading at the portfolio level. This book will be an indispensable resource for serious options traders working individually, in hedge funds, or in other institutions.